






import copy
import importlib.metadata
import json
import os
from dataclasses import dataclass
from typing import Any, Dict, Union

from packaging import version

from ..utils import is_torch_available, logging


if is_torch_available():
							import torch


UpperCamelCase								=		logging.get_logger(__name__)

@dataclass
class 				lowerCAmelCase_	:


				def __init__(					self ,						_lowerCAmelCase=False ,						_lowerCAmelCase=False ,						_lowerCAmelCase=6.0 ,						_lowerCAmelCase=None ,						_lowerCAmelCase=False ,						_lowerCAmelCase=False ,						_lowerCAmelCase=None ,						_lowerCAmelCase="fp4" ,						_lowerCAmelCase=False ,						**_lowerCAmelCase ,						):
											_lowercase							:   str 							=   load_in_abit
											_lowercase							:   List[Any] 							=   load_in_abit
											_lowercase							:   List[Any] 							=   llm_inta_threshold
											_lowercase							:   int 							=   llm_inta_skip_modules
											_lowercase							:   Optional[Any] 							=   llm_inta_enable_fpaa_cpu_offload
											_lowercase							:   List[str] 							=   llm_inta_has_fpaa_weight
											_lowercase							:   Tuple 							=   bnb_abit_quant_type
											_lowercase							:   Dict 							=   bnb_abit_use_double_quant

											if bnb_abit_compute_dtype is None:
																		_lowercase							:   Any 							=   torch.floataa
											elif isinstance(__SCREAMING_SNAKE_CASE ,						__SCREAMING_SNAKE_CASE      ):
																		_lowercase							:   List[Any] 							=   getattr(__SCREAMING_SNAKE_CASE ,						__SCREAMING_SNAKE_CASE      )
											elif isinstance(__SCREAMING_SNAKE_CASE ,						torch.dtype      ):
																		_lowercase							:   Optional[int] 							=   bnb_abit_compute_dtype
											else:
																		raise ValueError('bnb_4bit_compute_dtype must be a string or a torch.dtype'      )

											self.post_init()


				def 						__a					(					self      ):
											if not isinstance(self.llm_inta_threshold ,						__SCREAMING_SNAKE_CASE      ):
																		raise ValueError('llm_int8_threshold must be a float'      )

											if self.llm_inta_skip_modules is not None and not isinstance(self.llm_inta_skip_modules ,						__SCREAMING_SNAKE_CASE      ):
																		raise ValueError('llm_int8_skip_modules must be a list of strings'      )
											if not isinstance(self.llm_inta_enable_fpaa_cpu_offload ,						__SCREAMING_SNAKE_CASE      ):
																		raise ValueError('llm_int8_enable_fp32_cpu_offload must be a boolean'      )

											if not isinstance(self.llm_inta_has_fpaa_weight ,						__SCREAMING_SNAKE_CASE      ):
																		raise ValueError('llm_int8_has_fp16_weight must be a boolean'      )

											if self.bnb_abit_compute_dtype is not None and not isinstance(self.bnb_abit_compute_dtype ,						torch.dtype      ):
																		raise ValueError('bnb_4bit_compute_dtype must be torch.dtype'      )

											if not isinstance(self.bnb_abit_quant_type ,						__SCREAMING_SNAKE_CASE      ):
																		raise ValueError('bnb_4bit_quant_type must be a string'      )

											if not isinstance(self.bnb_abit_use_double_quant ,						__SCREAMING_SNAKE_CASE      ):
																		raise ValueError('bnb_4bit_use_double_quant must be a boolean'      )

											if self.load_in_abit and not version.parse(importlib.metadata.version('bitsandbytes'      )      ) >= version.parse(
											    '0.39.0'      ):
																		raise ValueError(
																		    '4 bit quantization requires bitsandbytes>=0.39.0 - please upgrade your bitsandbytes version'      )


				def 						__a					(					self      ):
											return self.load_in_abit or self.load_in_abit


				def 						__a					(					self      ):
											if self.load_in_abit:
																		return "llm_int8"
											elif self.load_in_abit and self.bnb_abit_quant_type == "fp4":
																		return "fp4"
											elif self.load_in_abit and self.bnb_abit_quant_type == "nf4":
																		return "nf4"
											else:
																		return None


				@classmethod
				def 						__a					(					cls ,						_lowerCAmelCase ,						_lowerCAmelCase ,						**_lowerCAmelCase      ):
											_lowercase							:   List[Any] 							=   cls(**__SCREAMING_SNAKE_CASE      )

											_lowercase							:   Optional[Any] 							=   []
											for key, value in kwargs.items():
																		if hasattr(__SCREAMING_SNAKE_CASE ,						__SCREAMING_SNAKE_CASE      ):
																									setattr(__SCREAMING_SNAKE_CASE ,						__SCREAMING_SNAKE_CASE ,						__SCREAMING_SNAKE_CASE      )
																									to_remove.append(__SCREAMING_SNAKE_CASE      )
											for key in to_remove:
																		kwargs.pop(__SCREAMING_SNAKE_CASE ,						__SCREAMING_SNAKE_CASE      )

											if return_unused_kwargs:
																		return config, kwargs
											else:
																		return config


				def 						__a					(					self ,						_lowerCAmelCase      ):
											with open(__SCREAMING_SNAKE_CASE ,						'w' ,						encoding='utf-8'      ) as writer:
																		_lowercase							:   str 							=   self.to_dict()
																		_lowercase							:   Any 							=   json.dumps(__SCREAMING_SNAKE_CASE ,						indent=2 ,						sort_keys=__SCREAMING_SNAKE_CASE      ) + '\n'

																		writer.write(__SCREAMING_SNAKE_CASE      )


				def 						__a					(					self      ):
											_lowercase							:   Optional[int] 							=   copy.deepcopy(self.__dict__      )
											_lowercase							:   List[str] 							=   str(output['bnb_4bit_compute_dtype']      ).split('.'      )[1]

											return output


				def __repr__(					self      ):
											return F"""{self.__class__.__name__} {self.to_json_string()}"""


				def 						__a					(					self ,						_lowerCAmelCase = True      ):
											if use_diff is True:
																		_lowercase							:   Dict 							=   self.to_diff_dict()
											else:
																		_lowercase							:   int 							=   self.to_dict()
											return json.dumps(__SCREAMING_SNAKE_CASE ,						indent=2 ,						sort_keys=__SCREAMING_SNAKE_CASE      ) + "\n"


				def 						__a					(					self      ):
											_lowercase							:   List[Any] 							=   self.to_dict()

											# get the default config dict
											_lowercase							:   int 							=   BitsAndBytesConfig().to_dict()

											_lowercase							:   Any 							=   {}

											# only serialize values that differ from the default config
											for key, value in config_dict.items():
																		if value != default_config_dict[key]:
																									_lowercase							:   Union[str, Any] 							=   value

											return serializable_config_dict












"""simple docstring"""





import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin






class 	a		(   __snake_case    , unittest.TestCase						):
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					DebertaTokenizer
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					True
    SCREAMING_SNAKE_CASE		:	Any		=					DebertaTokenizerFast






    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						Dict:
      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      lowerCamelCase_       	=		[
          'l',
          'o',
          'w',
          'e',
          'r',
          's',
          't',
          'i',
          'd',
          'n',
          '\u0120',
          '\u0120l',
          '\u0120n',
          '\u0120lo',
          '\u0120low',
          'er',
          '\u0120lowest',
          '\u0120newer',
          '\u0120wider',
          '[UNK]',
      ]
      lowerCamelCase_       	=		dict(zip(__SCREAMING_SNAKE_CASE			,		range(len(__SCREAMING_SNAKE_CASE   )   )   )   )
      lowerCamelCase_       	=		['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
      lowerCamelCase_       	=		{'unk_token': '[UNK]'}

      lowerCamelCase_       	=		os.path.join(self.tmpdirname			,		VOCAB_FILES_NAMES['vocab_file']   )
      lowerCamelCase_       	=		os.path.join(self.tmpdirname			,		VOCAB_FILES_NAMES['merges_file']   )
      with open(self.vocab_file			,		'w'			,		encoding='utf-8'   ) as fp:
        fp.write(json.dumps(__SCREAMING_SNAKE_CASE   ) + '\n'   )
      with open(self.merges_file			,		'w'			,		encoding='utf-8'   ) as fp:
        fp.write('\n'.join(__SCREAMING_SNAKE_CASE   )   )






    def   UpperCamelCase			(			self	:				Tuple			,		**__SCREAMING_SNAKE_CASE	:				Dict   )	->						List[Any]:
      kwargs.update(self.special_tokens_map   )
      return self.tokenizer_class.from_pretrained(self.tmpdirname			,		**__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				str			,		__SCREAMING_SNAKE_CASE	:				Any   )	->						Dict:
      lowerCamelCase_       	=		'lower newer'
      lowerCamelCase_       	=		'lower newer'
      return input_text, output_text






    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						Optional[Any]:
      lowerCamelCase_       	=		self.get_tokenizer()
      lowerCamelCase_       	=		'lower newer'
      lowerCamelCase_       	=		['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
      lowerCamelCase_       	=		tokenizer.tokenize(__SCREAMING_SNAKE_CASE   )
      self.assertListEqual(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		tokens + [tokenizer.unk_token]
      lowerCamelCase_       	=		[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE   )			,		__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Tuple:
      lowerCamelCase_       	=		self.get_tokenizer()
      lowerCamelCase_       	=		tokenizer('Hello'			,		'World'   )
      lowerCamelCase_       	=		[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
      self.assertListEqual(tokd['token_type_ids']			,		__SCREAMING_SNAKE_CASE   )






    @slow
    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Tuple:
      lowerCamelCase_       	=		self.tokenizer_class.from_pretrained('microsoft/deberta-base'   )

      lowerCamelCase_       	=		tokenizer.encode('sequence builders'			,		add_special_tokens=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		tokenizer.encode('multi-sequence build'			,		add_special_tokens=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		tokenizer.encode(
          'sequence builders'			,		add_special_tokens=__SCREAMING_SNAKE_CASE			,		add_prefix_space=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		tokenizer.encode(
          'sequence builders'			,		'multi-sequence build'			,		add_special_tokens=__SCREAMING_SNAKE_CASE			,		add_prefix_space=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

      assert encoded_sentence == encoded_text_from_decode
      assert encoded_pair == encoded_pair_from_decode






    @slow
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						Optional[Any]:
      lowerCamelCase_       	=		[self.tokenizer_class]
      if self.test_rust_tokenizer:
        tokenizer_classes.append(self.rust_tokenizer_class   )

      for tokenizer_class in tokenizer_classes:
        lowerCamelCase_       	=		tokenizer_class.from_pretrained('microsoft/deberta-base'   )

        lowerCamelCase_       	=		[
            'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
            'ALBERT incorporates two parameter reduction techniques',
            'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
            ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
            ' vocabulary embedding.',
        ]

        lowerCamelCase_       	=		tokenizer(__SCREAMING_SNAKE_CASE			,		padding=__SCREAMING_SNAKE_CASE   )
        lowerCamelCase_       	=		[tokenizer.decode(__SCREAMING_SNAKE_CASE			,		skip_special_tokens=__SCREAMING_SNAKE_CASE   ) for seq in encoding['input_ids']]

        # fmt: off
        lowerCamelCase_       	=		{
            'input_ids': [
                [1, 2118, 11126, 565, 35, 83, 25191, 163, 18854, 13, 12156, 12, 16101, 25376, 13807, 9, 22205, 27893, 1635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [1, 2118, 11126, 565, 24536, 80, 43797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [1, 133, 78, 65, 16, 10, 3724, 1538, 33183, 11303, 43797, 1938, 4, 870, 24165, 29105, 5, 739, 32644, 33183, 11303, 36173, 88, 80, 650, 7821, 45940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 13171, 31, 5, 1836, 9, 32644, 33183, 11303, 4, 2]
            ],
            'token_type_ids': [
                [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
            ],
            'attention_mask': [
                [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
            ]
        }
        # fmt: on

        lowerCamelCase_       	=		[
            'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
            'ALBERT incorporates two parameter reduction techniques',
            'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
            ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
            ' vocabulary embedding.',
        ]

        self.assertDictEqual(encoding.data			,		__SCREAMING_SNAKE_CASE   )

        for expected, decoded in zip(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ):
          self.assertEqual(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )




